Operationalizing Downstream Product Data for Improved Early Design Processes

新产品和系统的设计通常需要集中和量身定制的设计方法，以提高产品性能的特定方面。一些感兴趣的设计方面是改善环境可持续性的设计，降低制造复杂性的设计，以及提高可靠性的设计。这些方法被称为design -for- x （DfX）方法，通常应用于传统设计周期的最后，在选定的概念被详细说明之后。从这个意义上说，DfX目标通常被认为是对设计过程的事后补充，需要潜在的昂贵迭代和进一步的设计改进。这是对工业竞争力的拖累。这项研究将通过操作这些下游DfX方法的数据，使DfX决策能够在设计过程的早期做出。由此产生的数据驱动设计方法将产生更智能的早期设计阶段设计过程，从而产生更高质量、更好性能、更经济、更环保的工程产品。本研究的目的是建立设计决策、产品功能和下游DfX数据之间的关系，并将这些关系作为新的数据驱动设计过程的基础，从而鼓励更智能的设计过程。目标将通过启用产品功能评估，创建自动化功能建模的方法，以及为俄勒冈州立大学的所有产品生成功能数据来实现？设计工程实验室产品存储库。当研究推力2完成后，设计人员将能够直接量化设计过程中早期设计决策的下游影响。这将减少昂贵和耗时的迭代，并提供对设计选择的见解，否则将不知道。研究推力3将通过使用环境设计数据集来测试我们的方法，并评估提供知情的前期设计决策对减少下游环境影响的效果。通过坚持开放科学原则，资源将广泛提供，以增加其潜在影响，包括开放获取/公开获取的期刊和会议论文、数字媒体、存储库访问以及数据和代码共享。这项工作将通过开发和应用设计自动化技术来提高设计的基础知识，以克服这个相对较新的研究领域的一个重大缺陷：缺乏关于如何在整个早期设计过程中使用与DfX影响相关的可量化信息（例如，可持续性指标）的知识。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The design of new products and systems often requires focused and tailored design approaches that improve specific aspects of product performance. Some of these design aspects of interest are design for improved environmental sustainability, design for reduced manufacturing complexity, and design for increased reliability. These methods, called Design-for-X (DfX) approaches, are typically applied at the end of a traditional design cycle, after a chosen concept has been detailed. In this sense, DfX objectives are traditionally considered after-the-fact additions to the design process, requiring potentially costly iteration and further design refinement. This represents a drag on industrial competitiveness. This research will enable DfX decision making early in a design process by operationalizing data from these downstream DfX approaches. The resulting data-driven design approach will yield smarter early-design-phase design processes that result in higher-quality, better-performing, more-economical and environmentally sustainable engineered products. The objective of this research is to establish the relationships between design decisions, product function, and downstream DfX data of interest, and to use these relationships as a foundation for new data-driven design processes, thus encouraging smarter design processes. Objectives will be achieved by enabling the assessment of product functionality, creating both a methodology for automating functional modeling, as well as generating functional data for all products in Oregon State University?s Design Engineering Lab Product Repository. When Research Thrust 2 is completed, designers will be able directly quantify the downstream impact of design decisions made early in the design process. This will reduce costly and time-consuming iteration and provide insight into design choices that would not have been otherwise known. Research Thrust 3 will test our method by using a Design for the Environment dataset, and assess how well providing informed, upfront design decisions will reduce downstream environmental impact. Through adherence to open science doctrine, resources will be made widely available to increase their potential impact, including open-access/publicly accessible journal and conference papers, digital media, repository access, and data and code sharing. This work will advance fundamental knowledge of design by developing and applying design automation techniques to overcome a significant shortcoming in this relatively new area of research: the lack of knowledge about how quantifiable information related to DfX impacts (e.g., sustainability metrics) can be used throughout the early design process.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.